Here we are restricting hierarchical GMM to only go through on level. We are comparing the cluster results to the gaba labels.
set.seed(314)
h2 <- hmc(sdat, maxDepth = 2, ccol = ccol, maxDim = 12)
h2lab <- viridis(max(h2$dat$labels$col))stackM(h2, ccol = ccol, centered = TRUE, depth = 1)cols <- c("black", "magenta")[gabaID$gaba+1]
acols <- alpha(cols, 0.35)
pairs(h2$dat$data, pch = 19, cex = 0.4, col = acols)acols2 <- alpha(h2lab[h2$dat$labels$col], 0.5)
pairs(h2$dat$data, pch = 19, cex = 0.4, col = acols2)p0 <- mclust::adjustedRandIndex(pred, gaba)
perms <- foreach(i = 1:1.5e4, .combine = c) %dopar% {
set.seed(i*2)
mclust::adjustedRandIndex(sample(pred), gaba)
}
pv0 <- sum(c(perms,p0) >= p0)/length(perms)hist(perms, xlim = c(min(perms), p0 + 0.25*p0),
main = "permutation test of ARI values", probability = TRUE)
#hist(perms, probability = TRUE)
abline(v = p0, col = 'red')| measurment | value |
|---|---|
| Misclassification Rate | 0.1437579 |
| Accuracy | 0.8562421 |
| Sensitivity | 0.2156863 |
| Specificity | 0.9002695 |
| Precision | 0.1294118 |
| Recall | 0.2156863 |
| ARI | 0.0731818 |
| \(p\)-value for ARI | 0.0135333 |
| F1-score | 0.1617647 |
| TP | 11 |
| FP | 74 |
| TN | 668 |
| FN | 40 |